In medical imaging, it is well-known to use a contrast agent to increase the intensity of the blood vessels as viewed in a computed tomography (CT) image. Subtraction is a frequently-used method of improving or clarifying the effect of contrast in a contrast-enhanced scan. A non-contrast scan (also known as a non-enhanced or pre-contrast scan) and a contrast-enhanced scan are obtained for a given vessel or vessels. The intensities of the non-contrast scan are subtracted from the intensities of the contrast-enhanced scan at each corresponding voxel location in the two scan volumes to remove features that are common to the contrast and the non-contrast scan (including, for example, bone and soft tissue) and to leave only the parts of the contrast image that have been enhanced by the contrast agent.
Some form of alignment between the data from the non-contrast scan and the data from the contrast-enhanced scan is usually necessary to ensure that the voxel locations of the two scans correspond anatomically.
Images may be aligned manually, also known as pixel shift. For example, a clinician may manually align contrast and pre-contrast images by overlaying the images and adjusting the position of one of the images relative to the other. Manual alignment is generally restricted to linear alignment and is dependent on the person aligning the images. However, manual alignment allows the clinician to see during the alignment process whether the contrast and pre-contrast images have been well-aligned, or whether there are any areas of the images in which the alignment is less good. The clinician's knowledge of the alignment process allows the clinician to judge whether the resulting subtraction image is reliable. If there are any artifacts in the subtraction image, the clinician may be able to determine whether such artifacts are a result of the alignment process, or whether they have a different cause.
Alternatively, the images may be aligned by mechanical alignment, for example in positron emission tomography-computed tomography (PCT/CT) or multispectral CT.
Modern systems may automatically align images using software. Using software allows images to be registered by linear or non-linear registration as appropriate. Software registration processes may be automatic, reproducible and allow more complex alignment methods than could be achieved by manual alignment. However, when subtraction images are generated by automatic registration, the clinician generally is not involved in the alignment of the images and therefore the clinician is unaware of the underlying accuracy of the alignment. The clinician may not readily be able to interpret artifacts in the subtraction image.
One application of subtraction of a non-contrast image from a contrast image is in imaging of the coronary arteries, known as coronary computed tomography angiography (CCTA). In CCTA, a contrast agent is introduced into the blood vessels. A non-contrast image is acquired when no contrast agent is present in the coronary arteries. A contrast image is acquired once the contrast agent is present in the coronary arteries. The contrast agent enhances the intensity of the coronary arteries.
In CCTA, subtraction is used to distinguish calcified plaque (calcifications) from the artery lumen. Subtraction may also be used to distinguish stents or any other similar high-intensity feature from the artery lumen. Bone is another high intensity feature that may be removed by subtraction.
In a non-contrast image, any calcification or stent has high intensity. The lumen has low intensity, which may be similar to the intensity of the surrounding soft tissue. In a contrast image, both the calcification or stent and the artery lumen have high intensity. In the case of the artery lumen, the high intensity is caused by the contrast agent. Calcifications, especially severe calcifications, impede the ability of a clinician to assess the CCTA data directly. The lumen is more difficult to interpret in the presence of calcified plaque, because both the lumen and the calcification appear as regions of high intensity.
It is desirable to subtract the non-contrast image from the contrast image. The subtraction removes calcifications or stents from the contrast image while retaining the enhancement of the lumen that is present in the contrast image.
FIG. 1(a) is a contrast (CCTA) image which includes a calcification 10 surrounding a vessel lumen 12. FIG. 1(b) shows a non-contrast image (for example, calcium score image) for the same vessel showing the same calcification. In the non-contrast image of FIG. 1(b) the lumen 12 may be difficult to distinguish from the background tissue because there is no enhancement of the lumen.
To remove the calcification 10 from the contrast image, the non-contrast image is subtracted from the contrast image by subtracting the intensities of the non-contrast data from the intensities of the contrast data at corresponding voxel locations in the two volumes.
FIG. 1(c) shows the subtraction image obtained by subtracting the image data corresponding to FIG. 1(b) from the image data corresponding to FIG. 1(a). In the subtraction image of FIG. 1(c), the lumen 12 may be seen more clearly than in the contrast image of FIG. 1(a) because the calcification 10 has been subtracted. Without the calcification 10, lumen 12 is easier to interpret, and the clinician can more easily estimate the lumen dimensions (for example, the lumen diameter) and the degree of stenosis.
It is known that artifacts may be present in subtracted image data. For example, in CT images in which calcium is subtracted, a common artifact is a dark spot or ring near the wall of a vessel. One possible cause of an artifact is a variation in intensity between the contrast scan and the non-contrast scan, which may be called a Hounsfield Unit (HU) variation. An HU variation artifact is not related to volume alignment, but instead is primarily a motion artifact related to the limitations of CT scanning technology. FIG. 2(a) is a subtraction image comprising an artifact 16 that is the result of HU variation.
An artifact may alternatively be caused by an error in registration. An error in alignment (for example, an error in software registration) may cause incorrect subtraction of HU values because the voxels in the contrast image are not correctly associated with the corresponding voxels in the non-contrast image. FIG. 2(b) is a subtraction image comprising an artifact 18 (a black region to the left of the lumen) that is the result of registration error, as well as artifacts caused by HU variation.
A further type of artifact that may be present in subtraction data is calcium blooming. Calcium blooming is an area of contrast-like intensity which may occur in an area of the image adjacent to the calcification. Calcium blooming makes the region of calcification look larger than its true physical extent. It is known that calcium blooming may be more significant in CCTA images than in non-contrast images.
Subtraction may also be used in applications other than those comparing contrast and non-contrast data. For example, subtraction may be used to compare images of perfusion.